论文标题
精湛：计算机视觉模型的培训平台
Masterful: A Training Platform for Computer Vision Models
论文作者
论文摘要
精湛的是一个训练深度学习计算机视觉模型的软件平台。数据和模型体系结构是平台的输入，输出是训练有素的模型。该平台的主要目标是最大化训练有素的模型的准确性，它通过其正规化和半监督的学习实现实现。该平台的次要目标是最大程度地减少调整训练超级计的通常所需的手动实验数量，该实验是通过多种金属学习算法实现的，这些算法是自定义的，这些算法是为了控制平台的正则化和半监督的学习实现。该平台的第三目标是最大程度地减少训练模型所需的计算资源，并通过另一组旨在控制TensorFlow的优化实现的构建的Metalerning算法实现。该平台以Tensorflow的数据管理，体系结构，自动差异化和优化实现为基础。
Masterful is a software platform to train deep learning computer vision models. Data and model architecture are inputs to the platform, and the output is a trained model. The platform's primary goal is to maximize a trained model's accuracy, which it achieves through its regularization and semi-supervised learning implementations. The platform's secondary goal is to minimize the amount of manual experimentation typically required to tune training hyperparameters, which it achieves via multiple metalearning algorithms which are custom built to control the platform's regularization and semi-supervised learning implementations. The platform's tertiary goal is to minimize the computing resources required to train a model, which it achieves via another set of metalearning algorithms which are purpose built to control Tensorflow's optimization implementations. The platform builds on top of Tensorflow's data management, architecture, automatic differentiation, and optimization implementations.
